{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T09:57:37Z","timestamp":1776419857584,"version":"3.51.2"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["SFRH\/BD\/139630\/2018"],"award-info":[{"award-number":["SFRH\/BD\/139630\/2018"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["LA\/P\/0063\/2020"],"award-info":[{"award-number":["LA\/P\/0063\/2020"]}]},{"DOI":"10.13039\/501100018711","name":"Center for Research and Development in Mathematics and Applications","doi-asserted-by":"crossref","award":["LA\/P\/0063\/2020"],"award-info":[{"award-number":["LA\/P\/0063\/2020"]}],"id":[{"id":"10.13039\/501100018711","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Being able to capture the characteristics of a time series with a feature vector is a very important task with a multitude of applications, such as classification, clustering or forecasting. Usually, the features are obtained from linear and nonlinear time series measures, that may present several data related drawbacks. In this work we introduce<jats:italic>NetF<\/jats:italic>as an alternative set of features, incorporating several representative topological measures of different complex networks mappings of the time series. Our approach does not require data preprocessing and is applicable regardless of any data characteristics. Exploring our novel feature vector, we are able to connect mapped network features to properties inherent in diversified time series models, showing that<jats:italic>NetF<\/jats:italic>can be useful to characterize time data. Furthermore, we also demonstrate the applicability of our methodology in clustering synthetic and benchmark time series sets, comparing its performance with more conventional features, showcasing how<jats:italic>NetF<\/jats:italic>can achieve high-accuracy clusters. Our results are very promising, with network features from different mapping methods capturing different properties of the time series, adding a different and rich feature set to the literature.<\/jats:p>","DOI":"10.1007\/s10618-022-00826-3","type":"journal-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T11:03:22Z","timestamp":1648724602000},"page":"1062-1101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Novel features for time series analysis: a complex networks approach"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9828-0757","authenticated-orcid":false,"given":"Vanessa Freitas","family":"Silva","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2972-2050","authenticated-orcid":false,"given":"Maria Eduarda","family":"Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5768-1383","authenticated-orcid":false,"given":"Pedro","family":"Ribeiro","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8411-7094","authenticated-orcid":false,"given":"Fernando","family":"Silva","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"issue":"1","key":"826_CR1","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1103\/RevModPhys.74.47","volume":"74","author":"R Albert","year":"2002","unstructured":"Albert R, Barab\u00e1si AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47. https:\/\/doi.org\/10.1103\/RevModPhys.74.47","journal-title":"Rev Mod Phys"},{"key":"826_CR2","unstructured":"Bagnall A, Lines J, Vickers W, Keogh E. The UEA & UCR time series classification repository. www.timeseriesclassification.com"},{"key":"826_CR3","volume-title":"Network science","author":"AL Barab\u00e1si","year":"2016","unstructured":"Barab\u00e1si AL (2016) Network science. Cambridge University Press, Cambridge"},{"key":"826_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.softx.2020.100456","volume":"11","author":"M Barandas","year":"2020","unstructured":"Barandas M, Folgado D, Fernandes L, Santos S, Abreu M, Bota P, Liu H, Schultz T, Gamboa H (2020) Tsfel: Time series feature extraction library. SoftwareX 11:100456. https:\/\/doi.org\/10.1016\/j.softx.2020.100456","journal-title":"SoftwareX"},{"key":"826_CR5","doi-asserted-by":"publisher","first-page":"44037","DOI":"10.1038\/srep44037","volume":"7","author":"FM Bianchi","year":"2017","unstructured":"Bianchi FM, Livi L, Alippi C, Jenssen R (2017) Multiplex visibility graphs to investigate recurrent neural network dynamics. Sci Rep 7:44037. https:\/\/doi.org\/10.1038\/srep44037","journal-title":"Sci Rep"},{"key":"826_CR6","doi-asserted-by":"publisher","unstructured":"Bonner S, Brennan J, Theodoropoulos G, Kureshi I, McGough AS (2016) Deep topology classification: a new approach for massive graph classification. In: IEEE international conference on Big Data. IEEE, pp 3290\u20133297. https:\/\/doi.org\/10.1109\/BigData.2016.7840988","DOI":"10.1109\/BigData.2016.7840988"},{"key":"826_CR7","volume-title":"Time series analysis: forecasting and control","author":"GE Box","year":"2015","unstructured":"Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, New York"},{"issue":"9","key":"826_CR8","doi-asserted-by":"publisher","first-page":"4917289","DOI":"10.1063\/1.4917289","volume":"25","author":"E Bradley","year":"2015","unstructured":"Bradley E, Kantz H (2015) Nonlinear time-series analysis revisited. Chaos 25(9):4917289. https:\/\/doi.org\/10.1063\/1.4917289","journal-title":"Chaos"},{"issue":"1","key":"826_CR9","doi-asserted-by":"publisher","first-page":"0102","DOI":"10.5540\/03.2017.005.01.0102","volume":"5","author":"A Campanharo","year":"2017","unstructured":"Campanharo A, Ramos F (2017) Distinguishing different dynamics in electroencephalographic time series through a complex network approach. Proc Ser Braz Soc Comput Appl Math 5(1):0102. https:\/\/doi.org\/10.5540\/03.2017.005.01.0102","journal-title":"Proc Ser Braz Soc Comput Appl Math"},{"key":"826_CR10","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.physa.2015.09.094","volume":"444","author":"AS Campanharo","year":"2016","unstructured":"Campanharo AS, Ramos FM (2016) Hurst exponent estimation of self-affine time series using quantile graphs. Physica A 444:43\u201348. https:\/\/doi.org\/10.1016\/j.physa.2015.09.094","journal-title":"Physica A"},{"issue":"8","key":"826_CR11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0023378","volume":"6","author":"AS Campanharo","year":"2011","unstructured":"Campanharo AS, Sirer MI, Malmgren RD, Ramos FM, Amaral LAN (2011) Duality between time series and networks. PLoS ONE 6(8):e23378. https:\/\/doi.org\/10.1371\/journal.pone.0023378","journal-title":"PLoS ONE"},{"key":"826_CR12","doi-asserted-by":"publisher","unstructured":"Chiu B, Keogh E, Lonardi S (2003) Probabilistic discovery of time series motifs. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 493\u2013498. https:\/\/doi.org\/10.1145\/956750.956808","DOI":"10.1145\/956750.956808"},{"key":"826_CR13","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.neucom.2018.03.067","volume":"307","author":"M Christ","year":"2018","unstructured":"Christ M, Braun N, Neuffer J, Kempa-Liehr AW (2018) Time series feature extraction on basis of scalable hypothesis tests (tsfresh-a python package). Neurocomputing 307:72\u201377. https:\/\/doi.org\/10.1016\/j.neucom.2018.03.067","journal-title":"Neurocomputing"},{"issue":"1","key":"826_CR14","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1080\/00018730601170527","volume":"56","author":"LdF Costa","year":"2007","unstructured":"Costa LdF, Rodrigues FA, Travieso G, Villas Boas PR (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56(1):167\u2013242. https:\/\/doi.org\/10.1080\/00018730601170527","journal-title":"Adv Phys"},{"issue":"3","key":"826_CR15","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1080\/00018732.2011.572452","volume":"60","author":"LdF Costa","year":"2011","unstructured":"Costa LdF, Oliveira ON Jr, Travieso G, Rodrigues FA, Villas Boas PR, Antiqueira L, Viana MP, Correa Rocha LE (2011) Analyzing and modeling real-world phenomena with complex networks: a survey of applications. Adv Phys 60(3):329\u2013412. https:\/\/doi.org\/10.1080\/00018732.2011.572452","journal-title":"Adv Phys"},{"key":"826_CR16","volume-title":"Time series analysis with applications in R","author":"JD Cryer","year":"2008","unstructured":"Cryer JD, Chan KS (2008) Time series analysis with applications in R. Springer, New York"},{"key":"826_CR17","unstructured":"Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Systems, 1695. http:\/\/igraph.org"},{"issue":"3\u20135","key":"826_CR18","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.physrep.2009.11.002","volume":"486","author":"S Fortunato","year":"2010","unstructured":"Fortunato S (2010) Community detection in graphs. Phys Rep 486(3\u20135):75\u2013174. https:\/\/doi.org\/10.1016\/j.physrep.2009.11.002","journal-title":"Phys Rep"},{"key":"826_CR19","doi-asserted-by":"crossref","unstructured":"Fulcher BD (2018) Feature-based time-series analysis. In: Feature engineering for machine learning and data analytics. CRC Press, pp 87\u2013116","DOI":"10.1201\/9781315181080-4"},{"issue":"12","key":"826_CR20","doi-asserted-by":"publisher","first-page":"3026","DOI":"10.1109\/TKDE.2014.2316504","volume":"26","author":"BD Fulcher","year":"2014","unstructured":"Fulcher BD, Jones NS (2014) Highly comparative feature-based time-series classification. IEEE Trans Knowl Data Eng 26(12):3026\u20133037. https:\/\/doi.org\/10.1109\/TKDE.2014.2316504","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"5","key":"826_CR21","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1016\/j.cels.2017.10.001","volume":"5","author":"BD Fulcher","year":"2017","unstructured":"Fulcher BD, Jones NS (2017) hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell Syst 5(5):527\u2013531. https:\/\/doi.org\/10.1016\/j.cels.2017.10.001","journal-title":"Cell Syst"},{"issue":"83","key":"826_CR22","doi-asserted-by":"publisher","first-page":"20130048","DOI":"10.1098\/rsif.2013.0048","volume":"10","author":"BD Fulcher","year":"2013","unstructured":"Fulcher BD, Little MA, Jones NS (2013) Highly comparative time-series analysis: the empirical structure of time series and their methods. J R Soc Interface 10(83):20130048. https:\/\/doi.org\/10.1098\/rsif.2013.0048","journal-title":"J R Soc Interface"},{"key":"826_CR23","doi-asserted-by":"publisher","unstructured":"Geurts P (2001) Pattern extraction for time series classification. In: European conference on principles of data mining and knowledge discovery. Springer, pp 115\u2013127. https:\/\/doi.org\/10.1007\/3-540-44794-6_10","DOI":"10.1007\/3-540-44794-6_10"},{"key":"826_CR24","doi-asserted-by":"publisher","first-page":"100","DOI":"10.2307\/2346830","volume":"28","author":"J Hartigan","year":"1979","unstructured":"Hartigan J, Wong M (1979) A k-means clustering algorithm. Appl Stat 28:100\u2013108","journal-title":"Appl Stat"},{"key":"826_CR25","unstructured":"Henderson T (2021) Rcatch22: Calculation of 22 CAnonical Time-Series CHaracteristics. R package version 0.1.13"},{"key":"826_CR26","doi-asserted-by":"crossref","unstructured":"Henderson T, Fulcher BD (2021) An empirical evaluation of time-series feature sets","DOI":"10.1109\/ICDMW53433.2021.00134"},{"key":"826_CR27","unstructured":"Hyndman R (2018) Mcomp: Data from the M-Competitions. https:\/\/CRAN.R-project.org\/package=Mcomp. R package version 2.8"},{"key":"826_CR28","doi-asserted-by":"crossref","unstructured":"Hyndman R, Kang Y, Montero-Manso P, Talagala T, Wang E, Yang Y, O\u2019Hara-Wild M (2020) tsfeatures: Time Series Feature Extraction. https:\/\/CRAN.R-project.org\/package=tsfeatures. R package version 1.0.2","DOI":"10.32614\/CRAN.package.tsfeatures"},{"issue":"4","key":"826_CR29","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1080\/00031305.1996.10473566","volume":"50","author":"RJ Hyndman","year":"1996","unstructured":"Hyndman RJ, Fan Y (1996) Sample quantiles in statistical packages. Am Stat 50(4):361\u2013365. https:\/\/doi.org\/10.1080\/00031305.1996.10473566","journal-title":"Am Stat"},{"key":"826_CR30","doi-asserted-by":"publisher","unstructured":"Hyndman RJ, Wang E, Laptev N (2015) Large-scale unusual time series detection. In: 2015 IEEE international conference on data mining workshop (ICDMW). IEEE, pp 1616\u20131619. https:\/\/doi.org\/10.1109\/ICDMW.2015.104","DOI":"10.1109\/ICDMW.2015.104"},{"key":"826_CR31","unstructured":"Instituto Brasileiro de Geografia e Estat\u00edstica - IBGE. https:\/\/www.ibge.gov.br"},{"key":"826_CR32","doi-asserted-by":"publisher","unstructured":"Kang Y, Hyndman RJ, Li F (2020) Gratis: Generating time series with diverse and controllable characteristics. Statistical Analysis and Data Mining: The ASA Data Science Journal. https:\/\/doi.org\/10.1002\/sam.11461","DOI":"10.1002\/sam.11461"},{"issue":"2","key":"826_CR33","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.ijforecast.2016.09.004","volume":"33","author":"Y Kang","year":"2017","unstructured":"Kang Y, Hyndman RJ, Smith-Miles K (2017) Visualising forecasting algorithm performance using time series instance spaces. Int J Forecast 33(2):345\u2013358. https:\/\/doi.org\/10.1016\/j.ijforecast.2016.09.004","journal-title":"Int J Forecast"},{"issue":"13","key":"826_CR34","doi-asserted-by":"publisher","first-page":"4972","DOI":"10.1073\/pnas.0709247105","volume":"105","author":"L Lacasa","year":"2008","unstructured":"Lacasa L, Luque B, Ballesteros F, Luque J, Nuno JC (2008) From time series to complex networks: the visibility graph. Proc Natl Acad Sci 105(13):4972\u20134975. https:\/\/doi.org\/10.1073\/pnas.0709247105","journal-title":"Proc Natl Acad Sci"},{"issue":"8","key":"826_CR35","doi-asserted-by":"publisher","first-page":"4927835","DOI":"10.1063\/1.4927835","volume":"25","author":"X Lan","year":"2015","unstructured":"Lan X, Mo H, Chen S, Liu Q, Deng Y (2015) Fast transformation from time series to visibility graphs. Chaos 25(8):4927835. https:\/\/doi.org\/10.1063\/1.4927835","journal-title":"Chaos"},{"issue":"6","key":"826_CR36","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.14778\/3447689.3447714","volume":"14","author":"X Li","year":"2021","unstructured":"Li X, Cheng R, Chang KCC, Shan C, Ma C, Cao H (2021) On analyzing graphs with motif-paths. Proceedings of the VLDB Endowment 14(6):1111\u20131123. https:\/\/doi.org\/10.14778\/3447689.3447714","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"6","key":"826_CR37","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1016\/j.physa.2010.11.027","volume":"390","author":"L L\u00fc","year":"2011","unstructured":"L\u00fc L, Zhou T (2011) Link prediction in complex networks: a survey. Physica A 390(6):1150\u20131170. https:\/\/doi.org\/10.1016\/j.physa.2010.11.027","journal-title":"Physica A"},{"issue":"6","key":"826_CR38","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1007\/s10618-019-00647-x","volume":"33","author":"CH Lubba","year":"2019","unstructured":"Lubba CH, Sethi SS, Knaute P, Schultz SR, Fulcher BD, Jones NS (2019) catch22: CAnonical Time-series CHaracteristics. Data Min Knowl Disc 33(6):1821\u20131852. https:\/\/doi.org\/10.1007\/s10618-019-00647-x","journal-title":"Data Min Knowl Disc"},{"issue":"4","key":"826_CR39","doi-asserted-by":"publisher","first-page":"046103","DOI":"10.1103\/PhysRevE.80.046103","volume":"80","author":"B Luque","year":"2009","unstructured":"Luque B, Lacasa L, Ballesteros F, Luque J (2009) Horizontal visibility graphs: exact results for random time series. Phys Rev E 80(4):046103. https:\/\/doi.org\/10.1103\/PhysRevE.80.046103","journal-title":"Phys Rev E"},{"key":"826_CR40","unstructured":"Maechler M, Fraley C, Leisch F, Reisen V, Lemonte A, Hyndman RJ (2020) fracdiff: Fractionally differenced ARIMA aka ARFIMA(p,d,q) models. https:\/\/CRAN.R-project.org\/package=fracdiff. R package version 1.5-1"},{"key":"826_CR41","doi-asserted-by":"publisher","DOI":"10.1201\/9780429058264","volume-title":"Time series clustering and classification","author":"EA Maharaj","year":"2019","unstructured":"Maharaj EA, D\u2019Urso P, Caiado J (2019) Time series clustering and classification. CRC Press, Boca Raton"},{"issue":"4","key":"826_CR42","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.physrep.2013.08.002","volume":"533","author":"FD Malliaros","year":"2013","unstructured":"Malliaros FD, Vazirgiannis M (2013) Clustering and community detection in directed networks: a survey. Phys Rep 533(4):95\u2013142. https:\/\/doi.org\/10.1016\/j.physrep.2013.08.002","journal-title":"Phys Rep"},{"issue":"1","key":"826_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v062.i01","volume":"62","author":"P Montero","year":"2014","unstructured":"Montero P, Vilar JA (2014) TSclust: An R package for time series clustering. J Stat Softw 62(1):1\u201343. https:\/\/doi.org\/10.18637\/jss.v062.i01","journal-title":"J Stat Softw"},{"issue":"1","key":"826_CR44","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.ijforecast.2019.02.011","volume":"36","author":"P Montero-Manso","year":"2020","unstructured":"Montero-Manso P, Athanasopoulos G, Hyndman RJ, Talagala TS (2020) FFORMA: feature-based forecast model averaging. Int J Forecast 36(1):86\u201392. https:\/\/doi.org\/10.1016\/j.ijforecast.2019.02.011","journal-title":"Int J Forecast"},{"key":"826_CR45","unstructured":"O\u2019Hara-Wild M, Hyndman R, Wang E (2021) feasts: Feature extraction and statistics for time series. https:\/\/CRAN.R-project.org\/package=feasts. R package version 0.2.1"},{"issue":"7","key":"826_CR46","doi-asserted-by":"publisher","first-page":"e0220061","DOI":"10.1371\/journal.pone.0220061","volume":"14","author":"S Oldham","year":"2019","unstructured":"Oldham S, Fulcher B, Parkes L, Arnatkevi\u010di\u016bt\u0117 A, Suo C, Fornito A (2019) Consistency and differences between centrality measures across distinct classes of networks. PLoS ONE 14(7):e0220061. https:\/\/doi.org\/10.1371\/journal.pone.0220061","journal-title":"PLoS ONE"},{"issue":"4","key":"826_CR47","doi-asserted-by":"publisher","first-page":"100227","DOI":"10.1016\/j.patter.2021.100227","volume":"2","author":"RL Peach","year":"2021","unstructured":"Peach RL, Arnaudon A, Schmidt JA, Palasciano HA, Bernier NR, Jelfs KE, Yaliraki SN, Barahona M (2021) HCGA: highly comparative graph analysis for network phenotyping. Patterns 2(4):100227. https:\/\/doi.org\/10.1016\/j.patter.2021.100227","journal-title":"Patterns"},{"key":"826_CR48","doi-asserted-by":"publisher","unstructured":"Pons P, Latapy M (2005) Computing communities in large networks using random walks. In: International symposium on computer and information sciences. Springer Berlin Heidelberg, pp 284\u2013293. https:\/\/doi.org\/10.1007\/11569596_31","DOI":"10.1007\/11569596_31"},{"key":"826_CR49","unstructured":"R Core Team (2020) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https:\/\/www.R-project.org\/"},{"issue":"7","key":"826_CR50","doi-asserted-by":"publisher","first-page":"3308505","DOI":"10.1063\/1.3308505","volume":"96","author":"ZG Shao","year":"2010","unstructured":"Shao ZG (2010) Network analysis of human heartbeat dynamics. Appl Phys Lett 96(7):3308505. https:\/\/doi.org\/10.1063\/1.3308505","journal-title":"Appl Phys Lett"},{"key":"826_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-52452-8","volume-title":"Time series analysis and its applications","author":"RH Shumway","year":"2017","unstructured":"Shumway RH, Stoffer DS (2017) Time series analysis and its applications. Springer, Berlin"},{"issue":"3","key":"826_CR52","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1111\/j.1467-9892.2004.01685.x","volume":"25","author":"ME Silva","year":"2004","unstructured":"Silva ME, Oliveira VL (2004) Difference equations for the higher-order moments and cumulants of the INAR(1) model. J Time Ser Anal 25(3):317\u2013333. https:\/\/doi.org\/10.1111\/j.1467-9892.2004.01685.x","journal-title":"J Time Ser Anal"},{"key":"826_CR53","unstructured":"Silva VF (2018) Time series analysis based on complex networks. MSc thesis, University of Porto"},{"issue":"3","key":"826_CR54","doi-asserted-by":"publisher","first-page":"1404","DOI":"10.1002\/widm.1404","volume":"11","author":"VF Silva","year":"2021","unstructured":"Silva VF, Silva ME, Ribeiro P, Silva F (2021) Time series analysis via network science: concepts and algorithms. WIREs Data Min Knowl Discov 11(3):1404. https:\/\/doi.org\/10.1002\/widm.1404","journal-title":"WIREs Data Min Knowl Discov"},{"key":"826_CR55","unstructured":"Talagala TS, Hyndman RJ, Athanasopoulos G et\u00a0al (2018) Meta-learning how to forecast time series. Monash Econometrics and Business Statistics Working Papers 6:18"},{"issue":"5","key":"826_CR56","doi-asserted-by":"publisher","first-page":"50002","DOI":"10.1209\/0295-5075\/97\/50002","volume":"97","author":"L Telesca","year":"2012","unstructured":"Telesca L, Lovallo M (2012) Analysis of seismic sequences by using the method of visibility graph. EPL (Europhys Lett) 97(5):50002. https:\/\/doi.org\/10.1209\/0295-5075\/97\/50002","journal-title":"EPL (Europhys Lett)"},{"issue":"2","key":"826_CR57","doi-asserted-by":"publisher","first-page":"107","DOI":"10.4310\/SII.2011.v4.n2.a1","volume":"4","author":"H Tong","year":"2011","unstructured":"Tong H (2011) Threshold models in time series analysis\u201430 years on. Stat Interface 4(2):107\u2013118. https:\/\/doi.org\/10.4310\/SII.2011.v4.n2.a1","journal-title":"Stat Interface"},{"key":"826_CR58","doi-asserted-by":"publisher","DOI":"10.1002\/9780470644560","volume-title":"Analysis of financial time series","author":"RS Tsay","year":"2010","unstructured":"Tsay RS (2010) Analysis of financial time series, 3rd edn. Wiley, New York","edition":"3"},{"key":"826_CR59","doi-asserted-by":"crossref","unstructured":"Vespignani A (2018) Twenty years of network science","DOI":"10.1038\/d41586-018-05444-y"},{"issue":"3","key":"826_CR60","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s10618-005-0039-x","volume":"13","author":"X Wang","year":"2006","unstructured":"Wang X, Smith K, Hyndman RJ (2006) Characteristic-based clustering for time series data. Data Min Knowl Discov 13(3):335\u2013364. https:\/\/doi.org\/10.1007\/s10618-005-0039-x","journal-title":"Data Min Knowl Discov"},{"key":"826_CR61","unstructured":"Witowski V, Foraita DR (2014) HMMpa: Analysing accelerometer data using hidden Markov models. https:\/\/CRAN.R-project.org\/package=HMMpa. R package version 1.0"},{"key":"826_CR62","unstructured":"Wuertz D, Setz T, Chalabi Y (2017) timeSeries: Rmetrics - Financial Time Series Objects. https:\/\/CRAN.R-project.org\/package=timeSeries. R package version 3042.102"},{"key":"826_CR63","unstructured":"Wuertz D, Setz T, Chalabi Y, Boudt C, Chausse P, Miklovac M (2017) fGarch: Rmetrics - Autoregressive Conditional Heteroskedastic Modelling. https:\/\/CRAN.R-project.org\/package=fGarch. R package version 3042.83"},{"issue":"6","key":"826_CR64","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1109\/JBHI.2014.2303991","volume":"18","author":"G Zhu","year":"2014","unstructured":"Zhu G, Li Y, Wen PP (2014) Analysis and classification of sleep stages based on difference visibility graphs from a single-channel eeg signal. IEEE J Biomed Health Inform 18(6):1813\u20131821. https:\/\/doi.org\/10.1109\/JBHI.2014.2303991","journal-title":"IEEE J Biomed Health Inform"},{"key":"826_CR65","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2018.10.005","volume":"787","author":"Y Zou","year":"2019","unstructured":"Zou Y, Donner RV, Marwan N, Donges JF, Kurths J (2019) Complex network approaches to nonlinear time series analysis. Phys Rep 787:1\u201397. https:\/\/doi.org\/10.1016\/j.physrep.2018.10.005","journal-title":"Phys Rep"},{"key":"826_CR66","volume-title":"Hidden Markov models for time series: an introduction using R","author":"W Zucchini","year":"2016","unstructured":"Zucchini W, MacDonald IL, Langrock R (2016) Hidden Markov models for time series: an introduction using R. Chapman and Hall\/CRC, Boca Raton"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00826-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-022-00826-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00826-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T10:22:17Z","timestamp":1726914137000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-022-00826-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,31]]},"references-count":66,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["826"],"URL":"https:\/\/doi.org\/10.1007\/s10618-022-00826-3","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,31]]},"assertion":[{"value":"12 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}